Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
-
(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
-
Discrete geometric representations such as meshes are a crucial part of engineering simulation pipelines. The success and fidelity of numerical methods heavily depend on the accurate representation
-
Master/engineer degree in computer science, applied mathematics, data science with background in image processing, imaging inverse problems, deep learning and optimisation. Good coding skills for numerical
-
, or examples, these aspects are of utmost importance and need to be explored to provide convincing and well-grounded arguments [1]. This PhD program will propose to explore advanced methods to detect implicit
-
Exploit an existing clinical database containing numerous follow-up FDG/PET exams o Develop modeling methods for the time-series analysis of PET-CT data o Develop a methodological formalism for integrating
-
the geometrical variability in imaging data. During the project, the candidate will: o Exploit an existing clinical database containing numerous follow-up FDG/PET exams o Develop modeling methods for the time
-
, which performs numerical analytics during the simulation. This is necessary due to the ever-growing gap between file system bandwidth and compute capacities. To this end, we are developing the Deisa
-
Enthusiasm for applying advanced data science methods including AI-based approaches to environmental challenges and scientific evidence synthesis Technical Skills: Extensive experience in managing and
-
Calatroni, and Laure Blanc-F´eraud. Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers. In Scale Space and Variational Methods in Computer Vision, pages 498–510, Cham